Please use this identifier to cite or link to this item:
Type: Artigo de periódico
Title: Face Identification Using Large Feature Sets.
Author: Schwartz, William Robson
Guo, Huimin
Choi, Jonghyun
Davis, Larry S
Abstract: With the goal of matching unknown faces against a gallery of known people, the face identification task has been studied for several decades. There are very accurate techniques to perform face identification in controlled environments, particularly when large numbers of samples are available for each face. However, face identification under uncontrolled environments or with a lack of training data is still an unsolved problem. We employ a large and rich set of feature descriptors (with more than 70,000 descriptors) for face identification using partial least squares to perform multichannel feature weighting. Then, we extend the method to a tree-based discriminative structure to reduce the time required to evaluate probe samples. The method is evaluated on Facial Recognition Technology (FERET) and Face Recognition Grand Challenge (FRGC) data sets. Experiments show that our identification method outperforms current state-of-the-art results, particularly for identifying faces acquired across varying conditions.
Subject: Algorithms
Artificial Intelligence
Image Enhancement
Image Interpretation, Computer-assisted
Information Storage And Retrieval
Pattern Recognition, Automated
Reproducibility Of Results
Sensitivity And Specificity
Subtraction Technique
Citation: Ieee Transactions On Image Processing : A Publication Of The Ieee Signal Processing Society. v. 21, n. 4, p. 2245-55, 2012-Apr.
Rights: fechado
Identifier DOI: 10.1109/TIP.2011.2176951
Date Issue: 2012
Appears in Collections:Unicamp - Artigos e Outros Documentos

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.